510_FinalProject

library(limma)
library(Glimma)
library(edgeR)
library(dplyr)
library(magrittr)
setwd('~/Desktop/USC/Fall_2020/TRGN_510/510_FinalProject')

Reading in count and metadata

# Reading in count data
count_matrix = read.table(file = 'count_processed.txt',sep = '\t',header = T,stringsAsFactors = F, check.names = F, row.names = 1)
count_matrix = count_matrix[,order(names(count_matrix))]
  

#Reading in clinical metadata
clinical_metadata = read.table(file = 'clinical_processed.txt', sep = '\t', header = T, as.is = T, check.names = F)
clinical_metadata = clinical_metadata[!duplicated(clinical_metadata[ , c("case_submitter_id")]),]

# Reading in exposure metadata
exposure_metadata = read.csv(file = 'exposure_processed.txt', sep = '\t', header = T, as.is = T, check.names = F)


# Merging metadata
all_metadata = merge(x = clinical_metadata, y = exposure_metadata, by = 'case_submitter_id')

all_metadata$ethnicity <- as.factor(all_metadata$ethnicity)
all_metadata$gender <- as.factor(all_metadata$gender)
all_metadata%<>%
  mutate(cancer_stage=case_when(
    ajcc_pathologic_stage %in% c("Stage IA","Stage IB") ~ c("Early_Stage"),
    ajcc_pathologic_stage %in% c("Stage IIIA") ~ c("Late_Stage")
  ))
all_metadata$cancer_stage = as.factor(all_metadata$cancer_stage)
all_metadata$race <- as.factor(all_metadata$race)
rownames(all_metadata) <- all_metadata$case_submitter_id
all_metadata = all_metadata[,-1]
all_metadata = all_metadata[order(rownames(all_metadata)),]

# Creating DGEList Object
geneExpr = DGEList(counts = count_matrix, samples = all_metadata)
geneExpr$samples$group=all_metadata$cancer_stage
geneid = rownames(geneExpr)
# Importing gencode reference from DGC website 
gencode = read.table('gencode.gene.info.v22.tsv',sep = '\t',header = T,stringsAsFactors = F, check.names = F)
genes = gencode[geneid %in% gencode$gene_id, ]
genes = genes[,c("gene_id","gene_name","seqname")]
rownames(genes) = genes$gene_id
genes = genes[order(genes$gene_id),]
geneExpr$genes = genes[,-1]

Data pre-processing

Transformation from the raw-scale

cpm <- cpm(geneExpr)
lcpm <- cpm(geneExpr, log=TRUE)
L <- mean(geneExpr$samples$lib.size) * 1e-6
M <- median(geneExpr$samples$lib.size) * 1e-6
c(L,M)

[1] 61.08195 61.54625

The average library size for this dataset is about 61.1 million.

Removing genes that are lowly expressed

table(rowSums(geneExpr$counts==0)==75)

FALSE  TRUE 
54508  5975 

keep.exprs <- filterByExpr(geneExpr, group=geneExpr$samples$group)

Around 11% of genes in the dataset have zero counts across all 75 samples.

geneExpr <- geneExpr[keep.exprs,, keep.lib.sizes=FALSE]
dim(geneExpr)

[1] 22200    75

In this dataset, the median library size is 61.5 million and 10/61.5 is about 0.2, therefore the filterByExpr function keeps genes that have a CPM of 0.2 or more. With this cutoff the number of genes are reduced to 22200, about 36% of genes from what I started with.

lcpm.cutoff <- log2(10/M + 2/L)
library(RColorBrewer)
nsamples <- ncol(geneExpr)
col <- brewer.pal(nsamples, "Paired")
par(mfrow=c(1,2))
plot(density(lcpm[,1]), col=col[1], lwd=2, ylim=c(0,0.55), las=2, main="", xlab="")
title(main="A. Raw data", xlab="Log-cpm")

abline(v=lcpm.cutoff, lty=3)
for (i in 2:nsamples){
den <- density(lcpm[,i])
lines(den$x, den$y, col=col[i], lwd=2)
}

legend("topright", colnames(geneExpr$counts), text.col=col, bty="n")
lcpm_filtered <- cpm(geneExpr, log=TRUE)
plot(density(lcpm_filtered[,1]), col=col[1], lwd=2, ylim=c(0,0.26), las=2, main="", xlab="")

title(main="B. Filtered data", xlab="Log-cpm")
abline(v=lcpm.cutoff, lty=3)

for (i in 2:nsamples){
den <- density(lcpm_filtered[,i])
lines(den$x, den$y, col=col[i], lwd=2)
}
legend("topright", colnames(geneExpr$counts), text.col=col, bty="n")

This graph showed that before filtering the data, a large portion of genes within each samples are lowly-expressed with log-CPM values that are small or negative.

Normalising gene expression distributions

geneExpr = calcNormFactors(geneExpr, method = "TMM")
geneExpr$samples$norm.factors

 [1] 0.9785086 0.8625784 0.8796584 0.8812264 0.8256012 0.9278378 1.0666562 0.9798973 1.0034271 0.9241075
[11] 1.0469593 0.8420605 0.8854165 1.1961829 0.7917407 0.8625393 1.0674138 0.9536610 0.9047548 1.0526746
[21] 1.1272267 1.0631808 0.8590180 0.9240654 0.9259949 0.9106659 0.9152927 1.0820802 1.1904767 1.0444837
[31] 1.2772873 1.1621433 1.0838828 0.9312814 1.1063331 0.9038572 1.1660689 0.9380158 1.1845461 1.1811310
[41] 0.9207408 1.0575105 1.1456111 1.2413162 0.9851884 1.1413746 0.7821984 0.9128198 1.1965024 1.0310234
[51] 1.1906658 1.1263900 1.0230443 0.9491962 0.9063829 1.0315531 1.1154274 0.8257342 1.0373601 0.8870061
[61] 1.0894171 0.9319910 0.7755565 1.0884700 1.0880573 1.1071277 0.9377693 0.8765014 1.1553650 0.9017619
[71] 0.9542095 1.0624291 0.9419850 1.1428800 1.0763562

geneExpr_unnormalized <- geneExpr
geneExpr_unnormalized$samples$norm.factors <- 1
par(mfrow=c(1,2))
lcpm_unnormalized <- cpm(geneExpr_unnormalized, log=TRUE)
boxplot(lcpm_unnormalized, las=2, col=col, main="")
title(main="A. Example: Unnormalised data",ylab="Log-cpm")

geneExpr_normalized <- calcNormFactors(geneExpr_unnormalized)  
geneExpr_normalized$samples$norm.factors

 [1] 0.9785086 0.8625784 0.8796584 0.8812264 0.8256012 0.9278378 1.0666562 0.9798973 1.0034271 0.9241075
[11] 1.0469593 0.8420605 0.8854165 1.1961829 0.7917407 0.8625393 1.0674138 0.9536610 0.9047548 1.0526746
[21] 1.1272267 1.0631808 0.8590180 0.9240654 0.9259949 0.9106659 0.9152927 1.0820802 1.1904767 1.0444837
[31] 1.2772873 1.1621433 1.0838828 0.9312814 1.1063331 0.9038572 1.1660689 0.9380158 1.1845461 1.1811310
[41] 0.9207408 1.0575105 1.1456111 1.2413162 0.9851884 1.1413746 0.7821984 0.9128198 1.1965024 1.0310234
[51] 1.1906658 1.1263900 1.0230443 0.9491962 0.9063829 1.0315531 1.1154274 0.8257342 1.0373601 0.8870061
[61] 1.0894171 0.9319910 0.7755565 1.0884700 1.0880573 1.1071277 0.9377693 0.8765014 1.1553650 0.9017619
[71] 0.9542095 1.0624291 0.9419850 1.1428800 1.0763562

lcpm_normalized <- cpm(geneExpr_normalized, log=TRUE)
boxplot(lcpm_normalized, las=2, col=col, main="")
title(main="B. Example: Normalised data",ylab="Log-cpm")

This figure showed that after normalization the samples are more similar to each other.

Unsupervised clustering of samples

lcpm <- cpm(geneExpr, log=TRUE)
par(mfrow=c(1,2))
col.group <- geneExpr$samples$group
levels(col.group) <-  brewer.pal(nlevels(col.group), "Set1")

minimal value for n is 3, returning requested palette with 3 different levels

col.group <- as.character(col.group)

col.race <- geneExpr$samples$race
levels(col.race) <-  brewer.pal(nlevels(col.race), "Set2")
col.race <- as.character(col.race)

col.gender <- geneExpr$samples$gender
levels(col.gender) <-  brewer.pal(nlevels(col.gender), "Set3")

minimal value for n is 3, returning requested palette with 3 different levels

col.gender <- as.character(col.gender)

col.age <- geneExpr$samples$age_at_diagnosis
levels(col.age) <-  brewer.pal(nlevels(col.age), "Set1")

minimal value for n is 3, returning requested palette with 3 different levels

col.age <- as.character(col.age)

col.ethnicity <- geneExpr$samples$ethnicity
levels(col.ethnicity) <-  brewer.pal(nlevels(col.ethnicity), "Set2")
col.ethnicity <- as.character(col.ethnicity)

col.cigar_per_day <- geneExpr$samples$cigarettes_per_day
levels(col.cigar_per_day) <-  brewer.pal(nlevels(col.cigar_per_day), "Set3")

minimal value for n is 3, returning requested palette with 3 different levels

col.cigar_per_day <- as.character(col.cigar_per_day)

plotMDS(lcpm,labels=geneExpr$samples$group,  col=col.group)
title(main="A. Sample groups")

plotMDS(lcpm, labels=geneExpr$samples$gender, col=col.gender, dim=c(3,4))
title(main="B. Sex")

plotMDS(lcpm, labels=geneExpr$samples$age_at_diagnosis, col=col.age, dim=c(3,4))
title(main="C. Age at diagnosis")

plotMDS(lcpm, labels=geneExpr$samples$race, col=col.race, dim=c(3,4))
title(main="D. Race")

plotMDS(lcpm, labels=geneExpr$samples$ethnicity, col=col.ethnicity, dim=c(3,4))
title(main="E. Ethnicity")

plotMDS(lcpm, labels=geneExpr$samples$cigarettes_per_day, col=col.cigar_per_day, dim=c(3,4))
title(main="F. Cigarettes per day")

In the MDS plot, there is no separation between group of patients with early stage lung cancer and late stage lung cancer.

Differential expression analysis

Creating a design matrix and contrasts

cancer_stage = geneExpr$samples$group
ethnicity = geneExpr$samples$ethnicity
sex = geneExpr$samples$gender
race = geneExpr$samples$race
age_at_diagnosis = geneExpr$samples$age_at_diagnosis
smoking_hist = geneExpr$samples$cigarettes_per_day

design = model.matrix(~0+cancer_stage)
colnames(design) <- gsub("cancer_stage", "", colnames(design))
contr.matrix <- makeContrasts(
   LatevsEarly = Late_Stage-Early_Stage, 
   levels = colnames(design))
contr.matrix

             Contrasts
Levels        LatevsEarly
  Early_Stage          -1
  Late_Stage            1

Removing heteroscedascity from count data

par(mfrow=c(1,2))
v <- voom(geneExpr, design, plot=TRUE)
v

An object of class "EList"
$genes

22195 more rows ...

$targets

70 more rows ...

$E
                   TCGA-18-3408 TCGA-18-3411 TCGA-18-3412 TCGA-18-3415 TCGA-21-5787 TCGA-22-1017 TCGA-22-4595
ENSG00000000003.13     6.940841     6.532156     9.623306     6.405694     6.161866     6.011719     5.382233
ENSG00000000419.11     6.127507     5.629602     5.873450     5.223988     5.947551     5.084264     5.157145
ENSG00000000457.12     4.915230     4.651094     4.033882     5.066546     3.082906     4.268798     4.352183
ENSG00000000460.15     3.766043     5.098553     3.575296     4.537560     3.193248     3.042742     5.344752
ENSG00000000938.11     3.165208     3.466478     3.359267     3.235769     3.748723     4.228450     4.028700
                   TCGA-22-4605 TCGA-22-5471 TCGA-22-5479 TCGA-33-4538 TCGA-33-4582 TCGA-33-4586 TCGA-33-4587
ENSG00000000003.13     6.256305     6.352014     5.674429     6.321893     6.250638     4.945963     6.519744
ENSG00000000419.11     5.317640     5.289686     5.499360     5.973730     6.385848     4.976280     5.923724
ENSG00000000457.12     3.704857     3.851032     4.320302     4.357968     3.949779     4.386887     4.596503
ENSG00000000460.15     2.681773     4.004931     3.805829     4.538968     4.502957     4.960812     4.822773
ENSG00000000938.11     5.208165     3.743941     3.607564     2.934619     1.811716     2.822609     2.657288
                   TCGA-33-6737 TCGA-33-6738 TCGA-33-AAS8 TCGA-33-AASB TCGA-33-AASL TCGA-34-5239 TCGA-34-5927
ENSG00000000003.13     6.675196     6.646394     5.458914    5.4573749     5.059398     4.511310     7.267688
ENSG00000000419.11     4.702174     5.753617     5.512720    4.8528825     5.593165     5.135761     5.150528
ENSG00000000457.12     3.294399     4.184599     3.324621    4.3069415     3.684446     4.956503     4.529386
ENSG00000000460.15     4.037452     3.930013     3.017486    3.6356632     3.554576     4.557067     4.001217
ENSG00000000938.11     4.528940     2.988233     2.595013    0.5957804     3.131616     4.188712     2.527074
                   TCGA-34-8454 TCGA-39-5024 TCGA-39-5029 TCGA-39-5040 TCGA-43-2576 TCGA-43-2581 TCGA-43-3394
ENSG00000000003.13     5.468944     6.190120     6.397925     4.135747     6.200619     5.411987     5.789420
ENSG00000000419.11     4.989187     5.506384     5.804568     5.951193     4.880227     4.975682     5.248206
ENSG00000000457.12     3.877007     4.119681     3.274461     4.062155     3.893746     3.018866     3.638461
ENSG00000000460.15     3.321025     4.041938     2.881286     4.001766     3.425071     3.730467     3.776506
ENSG00000000938.11     4.685236     3.447920     4.056115     2.551979     5.190415     4.776987     3.174014
                   TCGA-43-6143 TCGA-43-6770 TCGA-43-7658 TCGA-43-8118 TCGA-43-A56V TCGA-46-3768 TCGA-52-7622
ENSG00000000003.13     5.132932     6.353322     5.817178     5.320020     5.930055     5.833526     5.613357
ENSG00000000419.11     4.510830     5.331555     5.885544     5.561129     5.754788     5.964438     5.042165
ENSG00000000457.12     5.232975     3.464464     5.279136     3.308579     4.056518     4.031502     3.892459
ENSG00000000460.15     5.301640     3.506844     5.178376     3.216432     3.790919     4.967196     3.294950
ENSG00000000938.11     4.766751     4.144669     3.271432     5.185672     3.780614     4.358341     4.968013
                   TCGA-56-7222 TCGA-56-7223 TCGA-56-7579 TCGA-56-8625 TCGA-56-A4BY TCGA-58-8391 TCGA-58-8392
ENSG00000000003.13     6.516228    5.7830329     6.283973     5.007172     5.815532     5.849406     6.422522
ENSG00000000419.11     5.917521    5.2690849     5.463797     4.562695     5.033928     5.064415     4.629590
ENSG00000000457.12     3.596829    4.2466320     3.773919     3.972591     3.299839     4.147932     4.014601
ENSG00000000460.15     4.192393    5.0744464     3.292625     3.133295     2.967817     4.138938     3.900667
ENSG00000000938.11     3.236023    0.5468433     4.103414     5.482292     3.384662     2.815656     2.453604
                   TCGA-58-A46K TCGA-58-A46L TCGA-58-A46N TCGA-60-2715 TCGA-60-2720 TCGA-60-2724 TCGA-63-A5MB
ENSG00000000003.13     5.324155     4.684436     5.857685     4.831959     6.489568     6.499121     5.208026
ENSG00000000419.11     5.783085     5.883267     4.553129     4.453417     5.756081     5.756652     5.948903
ENSG00000000457.12     3.788554     4.572600     3.734360     3.580724     3.237980     3.169620     4.178051
ENSG00000000460.15     3.644777     4.068852     4.016449     2.178884     3.592568     3.314010     4.311646
ENSG00000000938.11     2.532781     3.491985     1.709738     5.367792     3.554725     3.237048     3.453966
                   TCGA-63-A5MI TCGA-63-A5ML TCGA-66-2757 TCGA-66-2758 TCGA-66-2759 TCGA-68-7756 TCGA-68-8251
ENSG00000000003.13     6.894384     5.318967     6.630237     5.460603     5.452989     6.090521     5.848058
ENSG00000000419.11     4.901205     5.663941     5.894340     5.835855     5.595445     5.742528     4.402366
ENSG00000000457.12     3.819402     4.048878     4.335104     3.563054     3.942836     3.438467     3.931240
ENSG00000000460.15     4.273174     4.152726     4.368462     3.382843     4.356897     2.981325     4.354065
ENSG00000000938.11     2.311431     2.723752     3.093840     4.010393     6.138481     3.153456     3.453223
                   TCGA-68-A59I TCGA-70-6722 TCGA-77-6844 TCGA-77-8143 TCGA-77-8150 TCGA-85-6561 TCGA-85-6798
ENSG00000000003.13     6.115164     5.602154     5.963766     5.797571     5.800494     5.421308     5.832675
ENSG00000000419.11     5.163020     4.401588     4.886345     5.935158     4.917873     4.790574     4.013731
ENSG00000000457.12     3.905871     3.724808     3.934196     3.815429     3.481390     3.879444     3.682646
ENSG00000000460.15     3.417384     3.541478     3.457555     4.186796     3.622417     3.684543     3.670820
ENSG00000000938.11     3.577347     3.677204     3.499667     3.468927     3.516240     4.023889     2.476648
                   TCGA-85-7698 TCGA-85-7699 TCGA-92-8063 TCGA-94-7033 TCGA-98-8020 TCGA-LA-A7SW TCGA-MF-A522
ENSG00000000003.13     5.578014     5.889598     5.532038     5.866545     5.656188     5.893305     5.047086
ENSG00000000419.11     4.990627     5.390475     4.857045     4.874749     5.558504     5.811053     6.234572
ENSG00000000457.12     3.625101     4.320433     3.560501     3.680605     3.414522     4.071040     4.824025
ENSG00000000460.15     2.899593     5.211671     4.228530     3.172735     3.418540     4.669088     4.773232
ENSG00000000938.11     3.369699     4.293619     3.767169     4.100673     3.627397     3.170923     2.788765
                   TCGA-NC-A5HG TCGA-NC-A5HI TCGA-NC-A5HO TCGA-NC-A5HQ TCGA-NC-A5HT
ENSG00000000003.13    6.1088443     5.867636     5.661177     5.593425    6.7885562
ENSG00000000419.11    6.6965765     5.822300     6.053978     5.307678    6.0546652
ENSG00000000457.12    4.2637874     4.202847     4.069781     3.965104    3.1174809
ENSG00000000460.15    3.6898918     3.806323     3.385570     3.608808    3.2489430
ENSG00000000938.11    0.9774272     4.152449     3.592712     3.300177    0.5536344
22195 more rows ...

$weights
         [,1]     [,2]     [,3]     [,4]     [,5]     [,6]     [,7]     [,8]     [,9]    [,10]    [,11]
[1,] 2.188850 2.289146 2.351571 2.175336 2.292009 2.329547 2.384663 2.360963 2.384438 2.307988 2.311839
[2,] 1.942816 2.159471 2.186134 1.923226 2.163718 2.149964 2.294534 2.200885 2.240642 2.116802 2.190696
[3,] 1.286983 1.451121 1.569844 1.269477 1.456661 1.518621 1.671459 1.592670 1.656972 1.474206 1.495032
[4,] 1.193760 1.471387 1.454425 1.178097 1.477024 1.406155 1.692605 1.476789 1.538752 1.364169 1.515473
[5,] 1.067853 1.260676 1.288524 1.055048 1.265387 1.246496 1.458448 1.308009 1.363701 1.210694 1.298429
        [,12]    [,13]    [,14]    [,15]    [,16]    [,17]    [,18]    [,19]    [,20]    [,21]    [,22]
[1,] 2.299036 2.321639 2.448822 2.378926 2.281542 2.313808 2.329786 2.191564 2.459757 2.459609 2.300170
[2,] 2.102728 2.203231 2.350449 2.286882 2.148208 2.125141 2.150374 1.946788 2.402689 2.368508 2.175847
[3,] 1.456827 1.514080 1.873887 1.656116 1.436487 1.485375 1.519173 1.290542 1.931359 1.917831 1.472557
[4,] 1.348116 1.534847 1.758506 1.677020 1.456497 1.374691 1.406671 1.196970 1.950912 1.804415 1.492761
[5,] 1.197037 1.315089 1.573285 1.443672 1.248264 1.219640 1.246943 1.070453 1.722965 1.619495 1.278900
        [,23]    [,24]    [,25]    [,26]    [,27]    [,28]    [,29]    [,30]    [,31]    [,32]    [,33]
[1,] 2.404327 2.322701 2.364799 2.167988 2.312096 2.392487 2.480249 2.471779 2.458982 2.334732 2.260451
[2,] 2.323581 2.204839 2.265908 2.001692 2.191024 2.253925 2.408392 2.391898 2.367521 2.158859 2.121582
[3,] 1.731525 1.516535 1.617254 1.272793 1.495528 1.679921 2.020873 1.977521 1.915378 1.530639 1.402250
[4,] 1.751818 1.537345 1.638787 1.290230 1.515977 1.561958 1.915283 1.866730 1.801714 1.417388 1.421665
[5,] 1.515394 1.317237 1.407892 1.113020 1.298863 1.384640 1.735936 1.684245 1.616771 1.256240 1.219390
        [,34]    [,35]    [,36]    [,37]    [,38]    [,39]    [,40]    [,41]    [,42]    [,43]    [,44]
[1,] 2.337900 2.438138 2.348219 2.472915 2.216220 2.258701 2.375429 2.211128 2.334418 2.088315 2.455032
[2,] 2.227916 2.331344 2.181191 2.424055 2.063795 2.119420 2.226237 2.057665 2.158320 1.905172 2.396093
[3,] 1.552077 1.830650 1.562244 1.994441 1.335382 1.399492 1.632415 1.328490 1.529910 1.189301 1.912708
[4,] 1.572934 1.714225 1.446985 2.012447 1.353970 1.418859 1.514696 1.346958 1.416706 1.205173 1.932025
[5,] 1.348430 1.528826 1.282040 1.791521 1.164160 1.217064 1.342045 1.158514 1.255645 1.046022 1.702078
        [,45]    [,46]    [,47]    [,48]    [,49]    [,50]    [,51]    [,52]    [,53]    [,54]    [,55]
[1,] 2.272728 2.437576 2.186381 2.312219 2.391727 2.209533 2.282485 2.374679 2.391632 2.324084 2.366031
[2,] 2.062480 2.330487 1.939206 2.191180 2.252568 2.055746 2.076288 2.224919 2.252398 2.206933 2.267860
[3,] 1.408754 1.828731 1.283752 1.495765 1.677565 1.326336 1.425150 1.630333 1.677271 1.519738 1.620661
[4,] 1.303779 1.712155 1.190844 1.516218 1.559615 1.344767 1.318869 1.512711 1.559317 1.540602 1.642251
[5,] 1.159630 1.526834 1.065491 1.299070 1.382488 1.156749 1.172346 1.340258 1.382220 1.320038 1.410978
        [,56]    [,57]    [,58]    [,59]    [,60]     [,61]    [,62]    [,63]    [,64]    [,65]    [,66]
[1,] 2.337833 2.303325 2.401878 2.372735 2.181327 1.6275852 2.438828 2.234256 2.423804 2.416598 2.113202
[2,] 2.164189 2.179868 2.320196 2.278494 2.018272 1.4202595 2.332395 2.086225 2.307325 2.339605 1.933845
[3,] 1.537869 1.478712 1.723739 1.639318 1.288773 0.9055379 1.833008 1.360765 1.779912 1.768034 1.213001
[4,] 1.424145 1.498874 1.744522 1.660348 1.306486 0.9147185 1.716767 1.379797 1.662225 1.788864 1.229271
[5,] 1.262131 1.284156 1.508040 1.427886 1.125850 0.8232213 1.531273 1.184935 1.478852 1.552834 1.064795
        [,67]    [,68]    [,69]    [,70]    [,71]    [,72]    [,73]    [,74]    [,75]
[1,] 2.352067 2.051470 2.309777 2.291638 2.245654 2.334169 1.899979 2.328248 2.355714
[2,] 2.186867 1.859414 2.188073 2.090889 2.101967 2.157892 1.691135 2.213249 2.252015
[3,] 1.570973 1.155119 1.491067 1.442597 1.378732 1.529331 1.044545 1.529422 1.593155
[4,] 1.455531 1.170188 1.511440 1.334972 1.398076 1.416164 1.057101 1.550453 1.614286
[5,] 1.289488 1.018612 1.294961 1.185865 1.199820 1.255173 0.932025 1.328534 1.385861
22195 more rows ...

$design
  Early_Stage Late_Stage
1           1          0
2           0          1
3           1          0
4           1          0
5           0          1
70 more rows ...

vfit <- lmFit(v, design)
vfit <- contrasts.fit(vfit, contrasts=contr.matrix)
efit <- eBayes(vfit)
plotSA(efit, main="Final model: Mean-variance trend")

Examining the number of DE genes

summary(decideTests(efit))

       LatevsEarly
Down             0
NotSig       22200
Up               0

res = topTable(efit,sort.by = "P")
head(res)